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arXiv · 2006.04497

Learning under Invariable Bayesian Safety

Gal Bahar·Omer Ben-Porat·Kevin Leyton-Brown·Moshe Tennenholtz
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Abstract

A recent body of work addresses safety constraints in explore-and-exploit systems. Such constraints arise where, for example, exploration is carried out by individuals whose welfare should be balanced with overall welfare. In this paper, we adopt a model inspired by recent work on a bandit-like setting for recommendations. We contribute to this line of literature by introducing a safety constraint that should be respected in every round and determines that the expected value in each round is above a given threshold. Due to our modeling, the safe explore-and-exploit policy deserves careful planning, or otherwise, it will lead to sub-optimal welfare. We devise an asymptotically optimal algorithm for the setting and analyze its instance-dependent convergence rate.

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Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz. 2020-06-08. Learning under Invariable Bayesian Safety. https://arxiv.org/abs/2006.04497

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